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      <title>如何设计和实现分布式存储方案？ - 交互式测验</title>
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    <body>
      <div class="main-wrapper">
          <div class="quiz-container">
            <div class="quiz-header">
              <h1>如何设计和实现分布式存储方案？ - 交互式测验</h1>
              <p>检验你对文档内容的理解程度</p>
            </div>
            <div id="quiz-content">
                <div class="quiz-body">
                    <div class="progress-bar">
                        <div id="progress-indicator"></div>
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                <div class="quiz-footer" id="quiz-footer">
                  <button id="prev-btn" class="nav-btn">上一题</button>
                  <button id="hint-btn" class="nav-btn" style="background-color: #ffc107; color: #333; box-shadow: 0 4px 12px rgba(255,193,7,0.3);">提示</button>
                  <button id="next-btn" class="nav-btn">下一题</button>
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      </div>

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        const quizData = [
  {
    "question": "在一个需要存储海量用户数据的系统中，为了将数据均匀分布到不同的物理节点上，避免因新用户持续注册而导致某些节点负载过高（即“热点问题”），以下哪种数据分片策略最为合适？",
    "options": [
      "范围分片 (Range Sharding)",
      "哈希分片 (Hash Sharding)",
      "目录分片 (Directory-Based Sharding)",
      "同步分片 (Synchronous Sharding)"
    ],
    "correctAnswerIndex": 1,
    "explanation": "哈希分片通过对数据的某个字段（如用户ID）进行哈希计算，然后根据哈希值来分配数据。这种方式可以确保数据随机且均匀地分布在各个节点上，从而有效地避免了热点问题。相比之下，范围分片容易因为连续写入（如新用户ID递增）而产生热点。",
    "source": "文档中「2. 数据分片与存储结构设计」-「分片策略」部分",
    "hint": "文档中提到，有一种策略“可以有效避免热点问题，但不利于范围查询”。"
  },
  {
    "question": "某金融支付系统要求，一笔交易数据必须成功写入主节点和所有副本节点后，才能向用户返回“支付成功”的确认信息。这种设计优先保证了数据的强一致性，它采用的是哪种数据复制策略？",
    "options": [
      "异步复制 (Asynchronous Replication)",
      "最终复制 (Eventual Replication)",
      "同步复制 (Synchronous Replication)"
    ],
    "correctAnswerIndex": 2,
    "explanation": "同步复制策略要求数据在主节点写入的同时，必须立即被复制到从节点，并且只有在所有相关节点都确认写入成功后，操作才会返回成功。这确保了所有副本在任何时刻都保持数据一致，但会增加写入操作的延迟。",
    "source": "文档中「3. 数据复制与一致性模型」-「复制策略」部分",
    "hint": "思考一下，“同步”这个词在计算机科学中通常意味着什么？它往往与等待和协调有关。"
  },
  {
    "question": "在分布式系统中，Raft 和 Paxos 这类共识算法的核心目标是什么？",
    "options": [
      "加密节点间的通信数据以保证安全。",
      "在多个节点之间就某个值或操作顺序达成一致，以确保数据一致性。",
      "通过心跳机制检测出集群中的故障节点。",
      "将大文件拆分成小的数据块进行存储。"
    ],
    "correctAnswerIndex": 1,
    "explanation": "分布式共识算法（如Paxos/Raft）的主要作用是在一个可能发生网络分区或节点故障的分布式集群中，让多个节点能够对某项提议（例如，哪个事务先执行，哪个节点是主节点）达成唯一的、不可撤销的共识。这是实现系统高一致性的关键。",
    "source": "文档中「5. 分布式共识与元数据管理」-「分布式共识算法」部分",
    "hint": "这类算法常被用于实现如 Zookeeper、Etcd 这样的协调服务，它们的主要工作是什么？"
  },
  {
    "question": "当一个分布式存储系统的负载增加时，我们通过向集群中添加更多的服务器节点来提升其整体存储容量和处理能力。这种扩展方式被称为什么？",
    "options": [
      "垂直扩展 (Scale-Up)",
      "弹性伸缩 (Elastic Scaling)",
      "水平扩展 (Scale-Out)",
      "数据复制 (Replication)"
    ],
    "correctAnswerIndex": 2,
    "explanation": "水平扩展（Scale-Out）指的是通过增加更多的机器（节点）来扩展系统的能力。这是分布式系统设计的核心优势之一，因为它允许系统容量和性能随着业务增长而线性增加，避免了单个高性能服务器的成本和瓶颈限制。垂直扩展则是指提升单个服务器的硬件性能（如CPU、内存）。",
    "source": "文档中「7. 扩展性与弹性设计」-「水平扩展（Scale-Out）」部分",
    "hint": "想象一下，是把一栋楼盖得更高，还是在旁边再盖一栋楼？文档描述的是后者。"
  },
  {
    "question": "一个社交信息流服务为了追求高可用性和快速响应，允许用户在发布新动态后，其粉丝可能在几秒钟后才能看到。系统只保证在没有新的更新后，最终所有用户看到的内容是一致的。这描述了哪种一致性模型？",
    "options": [
      "强一致性 (Strong Consistency)",
      "因果一致性 (Causal Consistency)",
      "最终一致性 (Eventual Consistency)",
      "同步一致性 (Synchronous Consistency)"
    ],
    "correctAnswerIndex": 2,
    "explanation": "最终一致性模型不保证在任何时刻所有副本的数据都完全相同，但承诺如果数据不再发生变化，经过一段时间后，所有副本的最终数据会变得一致。这种模型通过牺牲部分实时一致性来换取更高的系统可用性和更低的延迟，非常适用于对实时性要求不那么极致的场景。",
    "source": "文档中「3. 数据复制与一致性模型」-「一致性模型」部分",
    "hint": "这个模型的关键在于，一致性不是即时的，而是“最终会达到”的。"
  },
  {
    "question": "根据文档，为了提升分布式存储系统的高性能读写能力，引入缓存机制（如Redis或Memcached）的主要目的是什么？",
    "options": [
      "增加数据的副本数量以提高容错性。",
      "对存储在磁盘上的数据进行加密。",
      "减少对慢速磁盘的I/O操作，从而提高数据读取速度。",
      "作为元数据管理中心来存储分片信息。"
    ],
    "correctAnswerIndex": 2,
    "explanation": "缓存机制的核心思想是将频繁访问的数据存储在高速介质（如内存）中。当系统需要读取数据时，首先检查缓存。如果数据在缓存中，就可以直接返回，避免了访问慢速磁盘的I/O操作，从而显著降低延迟，提高读取性能。",
    "source": "文档中「6. 高性能读写与优化策略」-「缓存机制」部分",
    "hint": "缓存通常位于应用和主存储之间，它利用了内存比磁盘快得多的特性。"
  },
  {
    "question": "如果你的应用场景是存储和分析海量的结构化数据，并且经常需要对特定的几列数据进行聚合计算（如求平均值、总和），哪种类型的存储结构设计最为高效？",
    "options": [
      "键值存储 (Key-Value Store)",
      "分布式文件系统 (Distributed File System)",
      "文档存储 (Document Store)",
      "列存储 (Column Store)"
    ],
    "correctAnswerIndex": 3,
    "explanation": "列存储（如Cassandra、HBase）将数据按列而不是按行进行存储。这种结构非常适合数据仓库和分析类应用，因为当查询只涉及少数几列时，系统只需读取相关列的数据，而无需加载整行，从而大大减少了I/O量，提高了查询效率。",
    "source": "文档中「2. 数据分片与存储结构设计」-「存储结构选择」部分",
    "hint": "文档中明确指出有一种存储结构“适用于需要高效读取大量列数据的场景”。"
  }
];
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            const answeredCount = userAnswers.filter(a => a !== null).length;
            const wrongCount = answeredCount - correctCount;
            const skippedCount = totalQuestions - answeredCount;
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            const errorRate = totalQuestions > 0 ? Math.round((wrongCount / totalQuestions) * 100) : 0;

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                '<h2>答题报告</h2>' +
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                '<div class="score-card">' +
                '<h3>得分</h3>' +
                '<p>' + correctCount + ' / ' + totalQuestions + '</p>' +
                '</div>' +
                '<div class="score-card">' +
                '<h3>正确率</h3>' +
                '<p>' + accuracy + '%</p>' +
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                '<div class="score-card">' +
                '<h3>错误率</h3>' +
                '<p>' + errorRate + '%</p>' +
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                '<div class="score-card score-details">' +
                '<table>' +
                '<tr><td>正确</td><td>' + correctCount + '</td></tr>' +
                '<tr><td>错误</td><td>' + wrongCount + '</td></tr>' +
                '<tr><td>未答</td><td>' + skippedCount + '</td></tr>' +
                '</table>' +
                '</div>' +
                '</div>' +
                '<div class="report-actions">' +
                '<button id="review-btn">回顾测验</button>' +
                '<button class="primary" onclick="location.reload()">再试一次</button>' +
                '</div>' +
                '</div>';

            let reportEl = document.querySelector('.final-score-container');
            if (reportEl) {
                reportEl.remove();
            }
            quizContentEl.insertAdjacentHTML('beforeend', finalScoreHtml);
            document.getElementById('review-btn').addEventListener('click', reviewQuiz);
        }

        function reviewQuiz() {
            const reportEl = document.querySelector('.final-score-container');
            if (reportEl) {
                reportEl.remove();
            }

            document.querySelector('.quiz-body').style.display = 'block';
            document.getElementById('quiz-footer').style.display = 'flex';
            
            currentQuestionIndex = 0;
            loadQuestion();
            updateNavigationButtons(); // Ensure nav buttons are correctly displayed
        }

        // Event Delegation for options
        optionsListEl.addEventListener('click', (event) => {
            if (event.target && event.target.matches('li.option-item')) {
                // Check if the question has already been answered
                if (userAnswers[currentQuestionIndex] === null) {
                    const selectedIndex = parseInt(event.target.dataset.index, 10);
                    selectOption(selectedIndex);
                }
            }
        });

        prevBtn.addEventListener('click', goToPreviousQuestion);
        nextBtn.addEventListener('click', goToNextQuestion);
        hintBtn.addEventListener('click', showHint);
        
        // 初始加载
        loadQuestion();
      </script>
    </body>
    </html>
  